CRAN Package Check Results for Package stminsights

Last updated on 2024-04-18 01:55:36 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.4.2 16.14 131.50 147.64 ERROR
r-devel-linux-x86_64-debian-gcc 0.4.2 11.68 97.49 109.17 OK
r-devel-linux-x86_64-fedora-clang 0.4.2 186.85 ERROR
r-devel-linux-x86_64-fedora-gcc 0.4.2 200.40 ERROR
r-prerel-macos-arm64 0.4.2 56.00 OK
r-prerel-windows-x86_64 0.4.2 12.00 102.00 114.00 OK
r-patched-linux-x86_64 0.4.2 8.85 126.11 134.96 ERROR
r-release-linux-x86_64 0.4.2 9.36 125.79 135.15 ERROR
r-release-macos-arm64 0.4.2 58.00 OK
r-release-macos-x86_64 0.4.2 113.00 OK
r-release-windows-x86_64 0.4.2 17.00 130.00 147.00 OK
r-oldrel-macos-arm64 0.4.2 48.00 OK
r-oldrel-windows-x86_64 0.4.2 17.00 144.00 161.00 OK

Check Details

Version: 0.4.2
Check: examples
Result: ERROR Running examples in ‘stminsights-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: get_network > ### Title: extract topic correlation network > ### Aliases: get_network > > ### ** Examples > > > library(stm) stm v1.3.7 successfully loaded. See ?stm for help. Papers, resources, and other materials at structuraltopicmodel.com > library(ggraph) Loading required package: ggplot2 > library(quanteda) Package version: 4.0.1 Unicode version: 15.0 ICU version: 72.1 Parallel computing: 3 of 32 threads used. See https://quanteda.io for tutorials and examples. > > # prepare data > data <- corpus(gadarian, text_field = 'open.ended.response') > docvars(data)$text <- as.character(data) > > data <- tokens(data, remove_punct = TRUE) |> + tokens_wordstem() |> + tokens_remove(stopwords('english')) |> dfm() |> + dfm_trim(min_termfreq = 2) > > out <- convert(data, to = 'stm') > > # fit model > gadarian_10 <- stm(documents = out$documents, + vocab = out$vocab, + data = out$meta, + prevalence = ~ treatment + s(pid_rep), + K = 10, + max.em.its = 1, # reduce computation time for example + verbose = FALSE) > > # extract network > stm_corrs <- get_network(model = gadarian_10, + method = 'simple', + labels = paste('Topic', 1:10), + cutoff = 0.001, + cutiso = TRUE) Error in get_network(model = gadarian_10, method = "simple", labels = paste("Topic", : There are no (sufficiently high) correlations between the topics of this STM model. Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 0.4.2
Check: examples
Result: ERROR Running examples in ‘stminsights-Ex.R’ failed The error most likely occurred in: > ### Name: get_network > ### Title: extract topic correlation network > ### Aliases: get_network > > ### ** Examples > > > library(stm) stm v1.3.7 successfully loaded. See ?stm for help. Papers, resources, and other materials at structuraltopicmodel.com > library(ggraph) Loading required package: ggplot2 > library(quanteda) Package version: 4.0.1 Unicode version: 13.0 ICU version: 69.1 Parallel computing: disabled See https://quanteda.io for tutorials and examples. > > # prepare data > data <- corpus(gadarian, text_field = 'open.ended.response') > docvars(data)$text <- as.character(data) > > data <- tokens(data, remove_punct = TRUE) |> + tokens_wordstem() |> + tokens_remove(stopwords('english')) |> dfm() |> + dfm_trim(min_termfreq = 2) > > out <- convert(data, to = 'stm') > > # fit model > gadarian_10 <- stm(documents = out$documents, + vocab = out$vocab, + data = out$meta, + prevalence = ~ treatment + s(pid_rep), + K = 10, + max.em.its = 1, # reduce computation time for example + verbose = FALSE) > > # extract network > stm_corrs <- get_network(model = gadarian_10, + method = 'simple', + labels = paste('Topic', 1:10), + cutoff = 0.001, + cutiso = TRUE) Error in get_network(model = gadarian_10, method = "simple", labels = paste("Topic", : There are no (sufficiently high) correlations between the topics of this STM model. Execution halted Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc